Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "48" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 51 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 49 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459866 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.904472 | 3.233092 | 23.091020 | 27.462191 | 2.078619 | 1.527409 | -1.184372 | -2.249529 | 0.6810 | 0.6715 | 0.4088 | nan | nan |
| 2459865 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.914315 | 3.896774 | 29.589316 | 36.936844 | 3.915458 | 6.198334 | 6.856215 | 5.697046 | 0.7010 | 0.6917 | 0.3825 | nan | nan |
| 2459864 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.830430 | 9.348158 | 24.431078 | 24.494026 | 5.483175 | 8.947288 | -5.934556 | -6.272246 | 0.6751 | 0.6532 | 0.4293 | nan | nan |
| 2459863 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.299793 | 5.307999 | 4.147923 | 4.022896 | 1.585015 | 2.104827 | -3.200759 | -4.023477 | 0.6720 | 0.6475 | 0.4164 | nan | nan |
| 2459862 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.908738 | 7.002335 | 29.464767 | 29.267047 | 7.478318 | 12.620876 | -2.536284 | -3.092680 | 0.6579 | 0.6842 | 0.4299 | nan | nan |
| 2459861 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 3.109155 | 3.716508 | 2.833104 | 2.473712 | 0.585942 | 0.217655 | -3.900575 | -4.402133 | 0.6868 | 0.6636 | 0.4294 | nan | nan |
| 2459860 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.590615 | 5.097160 | 22.435510 | 22.125633 | 10.323019 | 14.678188 | -2.299890 | -3.351786 | 0.6866 | 0.6562 | 0.4272 | nan | nan |
| 2459859 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 3.061670 | 3.130500 | 2.915110 | 2.464707 | 0.835001 | 0.216629 | -2.412524 | -2.922979 | 0.6933 | 0.6610 | 0.4226 | nan | nan |
| 2459858 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.042627 | 3.468368 | 2.634102 | 2.294387 | 0.585636 | -0.285007 | -4.229243 | -4.508992 | 0.7050 | 0.6687 | 0.4347 | 2.834957 | 2.638903 |
| 2459857 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 25.011257 | 24.319911 | 11.192288 | 11.234026 | 11.908282 | 12.644056 | 47.511844 | 49.794054 | 0.0329 | 0.0333 | 0.0001 | nan | nan |
| 2459856 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 6.456241 | 6.571342 | 20.579751 | 20.140595 | 4.427210 | 7.416480 | -3.855797 | -4.192125 | 0.6929 | 0.6816 | 0.4195 | 2.539507 | 2.367029 |
| 2459855 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 75.881435 | 75.479274 | inf | inf | 4363.644384 | 4363.637796 | 4165.170593 | 4164.518655 | 0.0078 | 0.0074 | 0.0008 | 0.000000 | 0.000000 |
| 2459854 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.091192 | 2.905401 | 12.795624 | 13.774311 | -0.109505 | 0.860952 | -1.663388 | -2.396952 | 0.7075 | 0.7389 | 0.4495 | 2.958963 | 2.546763 |
| 2459853 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.573063 | 2.390249 | 16.518311 | 17.670874 | 1.761878 | 2.120566 | -1.943922 | -3.095155 | 0.7241 | 0.6789 | 0.4453 | 3.467913 | 3.190201 |
| 2459852 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.751524 | 4.600523 | 16.019201 | 17.930810 | 7.600349 | 7.748574 | 10.669393 | 12.286929 | 0.8068 | 0.8115 | 0.2810 | 5.061433 | 5.313942 |
| 2459851 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.424535 | 5.721825 | 18.290166 | 18.712407 | 5.136981 | 19.531101 | 2.595111 | 12.554404 | 0.7383 | 0.7332 | 0.3645 | 2.947240 | 2.757466 |
| 2459850 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.756966 | 2.875865 | 15.622652 | 16.339374 | 2.289820 | 5.817607 | -0.162938 | 5.688537 | 0.7264 | 0.7462 | 0.3676 | 3.055441 | 2.934204 |
| 2459849 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.462089 | 3.252888 | 31.426852 | 32.725173 | 1.455625 | 1.917992 | -1.101786 | -1.991814 | 0.7257 | 0.7377 | 0.3724 | 2.666661 | 2.601147 |
| 2459848 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.249655 | 3.252682 | 22.563517 | 23.646458 | 2.939821 | 5.694635 | -1.884104 | -2.397765 | 0.7063 | 0.7439 | 0.3900 | 3.020885 | 2.916021 |
| 2459847 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.259168 | 2.967894 | 21.219578 | 22.555682 | 3.552427 | 9.952180 | -1.091694 | -2.153773 | 0.7107 | 0.6777 | 0.4466 | 2.294399 | 2.126464 |
| 2459846 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.323195 | 10.686406 | 16.743820 | 18.225408 | 10.055065 | 9.471941 | -0.553845 | -1.246931 | 0.8334 | 0.6628 | 0.5100 | 5.889580 | 2.806949 |
| 2459845 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.599176 | 3.884000 | 28.685894 | 30.209084 | 2.852849 | 3.243865 | -1.361721 | -2.842054 | 0.7304 | 0.7494 | 0.3811 | 0.000000 | 0.000000 |
| 2459844 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 30.002485 | 31.338418 | 77.490359 | 77.675570 | 9.441747 | 10.310696 | 26.926853 | 30.985971 | 0.0329 | 0.0312 | 0.0011 | nan | nan |
| 2459843 | not_connected | 100.00% | 0.66% | 0.66% | 0.00% | 100.00% | 0.00% | 8.571797 | 9.063615 | 18.894662 | 18.882324 | 9.044108 | 13.570397 | -3.467713 | -3.625519 | 0.7234 | 0.7304 | 0.4032 | 3.199406 | 2.977271 |
| 2459842 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.692769 | 1.065845 | 0.032863 | -0.545466 | -1.724607 | -1.924796 | -2.168986 | -2.150311 | 0.7231 | 0.6295 | 0.2847 | 2.815679 | 2.653751 |
| 2459841 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 63.247197 | 60.254380 | 72.102045 | 74.242901 | 58.350812 | 61.713181 | 43.956170 | 44.148228 | 0.0325 | 0.0318 | 0.0002 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 27.462191 | 3.233092 | 2.904472 | 27.462191 | 23.091020 | 1.527409 | 2.078619 | -2.249529 | -1.184372 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 36.936844 | 4.914315 | 3.896774 | 29.589316 | 36.936844 | 3.915458 | 6.198334 | 6.856215 | 5.697046 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 24.494026 | 9.348158 | 7.830430 | 24.494026 | 24.431078 | 8.947288 | 5.483175 | -6.272246 | -5.934556 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Shape | 5.307999 | 4.299793 | 5.307999 | 4.147923 | 4.022896 | 1.585015 | 2.104827 | -3.200759 | -4.023477 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | ee Power | 29.464767 | 5.908738 | 7.002335 | 29.464767 | 29.267047 | 7.478318 | 12.620876 | -2.536284 | -3.092680 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Shape | 3.716508 | 3.716508 | 3.109155 | 2.473712 | 2.833104 | 0.217655 | 0.585942 | -4.402133 | -3.900575 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | ee Power | 22.435510 | 4.590615 | 5.097160 | 22.435510 | 22.125633 | 10.323019 | 14.678188 | -2.299890 | -3.351786 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Shape | 3.130500 | 3.061670 | 3.130500 | 2.915110 | 2.464707 | 0.835001 | 0.216629 | -2.412524 | -2.922979 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Shape | 3.468368 | 3.468368 | 3.042627 | 2.294387 | 2.634102 | -0.285007 | 0.585636 | -4.508992 | -4.229243 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Discontinuties | 49.794054 | 24.319911 | 25.011257 | 11.234026 | 11.192288 | 12.644056 | 11.908282 | 49.794054 | 47.511844 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | ee Power | 20.579751 | 6.456241 | 6.571342 | 20.579751 | 20.140595 | 4.427210 | 7.416480 | -3.855797 | -4.192125 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | inf | 75.479274 | 75.881435 | inf | inf | 4363.637796 | 4363.644384 | 4164.518655 | 4165.170593 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 13.774311 | 2.905401 | 2.091192 | 13.774311 | 12.795624 | 0.860952 | -0.109505 | -2.396952 | -1.663388 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 17.670874 | 2.390249 | 1.573063 | 17.670874 | 16.518311 | 2.120566 | 1.761878 | -3.095155 | -1.943922 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 17.930810 | 2.751524 | 4.600523 | 16.019201 | 17.930810 | 7.600349 | 7.748574 | 10.669393 | 12.286929 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 19.531101 | 1.424535 | 5.721825 | 18.290166 | 18.712407 | 5.136981 | 19.531101 | 2.595111 | 12.554404 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 16.339374 | 1.756966 | 2.875865 | 15.622652 | 16.339374 | 2.289820 | 5.817607 | -0.162938 | 5.688537 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 32.725173 | 2.462089 | 3.252888 | 31.426852 | 32.725173 | 1.455625 | 1.917992 | -1.101786 | -1.991814 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 23.646458 | 3.252682 | 2.249655 | 23.646458 | 22.563517 | 5.694635 | 2.939821 | -2.397765 | -1.884104 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 22.555682 | 2.967894 | 2.259168 | 22.555682 | 21.219578 | 9.952180 | 3.552427 | -2.153773 | -1.091694 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 18.225408 | 9.323195 | 10.686406 | 16.743820 | 18.225408 | 10.055065 | 9.471941 | -0.553845 | -1.246931 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 30.209084 | 3.884000 | 3.599176 | 30.209084 | 28.685894 | 3.243865 | 2.852849 | -2.842054 | -1.361721 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 77.675570 | 30.002485 | 31.338418 | 77.490359 | 77.675570 | 9.441747 | 10.310696 | 26.926853 | 30.985971 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | ee Power | 18.894662 | 9.063615 | 8.571797 | 18.882324 | 18.894662 | 13.570397 | 9.044108 | -3.625519 | -3.467713 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | ee Shape | 1.692769 | 1.692769 | 1.065845 | 0.032863 | -0.545466 | -1.724607 | -1.924796 | -2.168986 | -2.150311 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 74.242901 | 63.247197 | 60.254380 | 72.102045 | 74.242901 | 58.350812 | 61.713181 | 43.956170 | 44.148228 |